fxy 0.5.6

Convenience imports and scientific functions.

fxy

Mnemonic imports and command fx with parameters to import libraries often used in research.

• f (For CALC - Basic calculator)

• x (For CAS software (“Numeric”) emulation)

• y (For LAB software (“Symbolic”) emulation )

Introduction

The people coming from use of CAS tools like Maple, Mathematica or computing LAB languages Matlab and R may find that Python requires quite a few imports just to do equivalent computing.

This package fxy is a shorthand to do the imports packages to approximate these two domains (CAS, and LAB) you’ve got a command fx, that starts Python with needed packages pre-imported: so, you can start using Python like a calculator right away.

Installation

• pip install fxy to get the import shortcuts.

Usage

The package defines the fx command, if you just want Python with something, run:

• $ fx -i[f|x|y]p - plain Python (i: “IPython on”, p: “Plotting on”)

Examples

In command line

• $ fx – calculator (equivalent to $fx -f

• $ fx -x– imports useful CAS functions (isympy+mpmath)

• $ fx -y– imports useful LAB functions (Stats, ML, Physics)

Additions:

• $ fx -i – calculator + IPython + explicit imports.

• $ fx -ip – calculator + plotting, with IPython.

E.g.,:

• $ fx -ip - calc with IPython, and plotting imports

• $ fx -ipx - CAS with IPython, and plotting imports

• $ fx -ipy - LAB with IPython, and plotting imports

Within notebooks and Python code

NB: This package does not assume versions of the imported packages, it just performs the basic imports, assuming that those namespaces within those packages will exist for a long time to come, so it is dependencies-agnostic.

CALC

>>> from fxy.calc import *
>>> pi
<pi: 3.14159~>

>>> from fxy.plot import *
>>> plt.plot([1, 2, 3, 4])
>>> plt.ylabel('some numbers')
>>> plt.show()

CAS

>>> from fxy.CAS import *
>>> f = x**4 - 4*x**3 + 4*x**2 - 2*x + 3
>>> f.subs([(x, 2), (y, 4), (z, 0)])
-1
>>> plot(f)
>>> plot3d(x**2-y**2)

LAB

>>> from fxy.LAB import *
>>> df = pandas.DataFrame({'x': numpy.arange(10), 'y': np.random.random(10)})
>>> df.sum()
x    45.000000
y     4.196558
dtype: float64

>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> neigh = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[0.66666667 0.33333333]]

Suggestions

If you use some initialization commonly, we suggest adding ~/.zshrc, something like, for example:

alias f=". ~/.venv/bin/activate && fx -if"

Or, pass params:

function f() {
    . ~/.venv/bin/activate
    fx "$@"
}

This way, running something like f makes a project folder and starts Python environment with import sets often used.